Posts Tagged ‘ model selection ’

structure and uncertainty, Bristol, Sept. 26

September 26, 2012
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structure and uncertainty, Bristol, Sept. 26

Another day full of interesting and challenging—in the sense they generated new questions for me—talks at the SuSTain workshop. After another (dry and fast) run around the Downs; Leo Held started the talks with one of my favourite topics, namely the theory of g-priors in generalized linear models. He did bring a new perspective on

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Simulation: The modeller’s laboratory

August 10, 2012
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Simulation: The modeller’s laboratory

In his 2004 paper in Trends in Ecology and Evolution, Steven Peck argues: Simulation models can be used to mimic complex systems, but unlike nature, can be manipulated in ways that would be impossible, too costly or unethical to do in natural systems. Simulation can add to theory development and testing, can offer hypotheses about the

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R script to calculate QIC for Generalized Estimating Equation (GEE) Model Selection

March 23, 2012
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R script to calculate QIC for Generalized Estimating Equation (GEE) Model Selection

Generalized Estimating Equations (GEE) can be used to analyze longitudinal count data; that is, repeated counts taken from the same subject or site. This is often referred to as repeated measures data, but longitudinal data often has more repeated obse...

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R script to calculate QIC for Generalized Estimating Equation (GEE) Model Selection

March 23, 2012
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R script to calculate QIC for Generalized Estimating Equation (GEE) Model Selection

Generalized Estimating Equations (GEE) can be used to analyze longitudinal count data; that is, repeated counts taken from the same subject or site. This is often referred to as repeated measures data, but longitudinal data often has more repeated observations. … Continue reading →

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Montreal R Workshop: Likelihood Methods and Model Selection

March 16, 2012
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Montreal R Workshop: Likelihood Methods and Model Selection

Monday, March 19, 2012  14h-16h, Stewart Biology N4/17 Corey Chivers, McGill University Department of Biology This workshop will introduce participants to the likelihood principal and its utility in statistical inference.  By learning how to formalize models through their likelihood function, participants will learn how to confront these models with data in order to make statistical

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